Convolutional Recurrent Neural Networks for Music Classification

Keunwoo Choi, George Fazekas, Mark Sandler, Kyunghyun Cho
9/14/2016
cs.NEcs.LGcs.MM

Official code: keunwoochoi/icassp_2017 (13 stars, last commit 2017-03-09).

Abstract

We introduce a convolutional recurrent neural network (CRNN) for music tagging. CRNNs take advantage of convolutional neural networks (CNNs) for local feature extraction and recurrent neural networks for temporal summarisation of the extracted features. We compare CRNN with three CNN structures that have been used for music tagging while controlling the number of parameters with respect to their performance and training time per sample. Overall, we found that CRNNs show a strong performance with respect to the number of parameter and training time, indicating the effectiveness of its hybrid structure in music feature extraction and feature summarisation.

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keunwoochoi/icassp_2017Official100%
132Oct 24, 20169 years ago

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Cite this paper

@article{choi2016convolutional,
  title  = {Convolutional Recurrent Neural Networks for Music Classification},
  author = {Keunwoo Choi and George Fazekas and Mark Sandler and Kyunghyun Cho},
  year   = {2016},
  eprint = {1609.04243},
  archivePrefix = {arXiv},
  url    = {https://arxiv.org/abs/1609.04243}
}

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